hypothesis
bandit
hypothesis | bandit | |
---|---|---|
20 | 21 | |
7,303 | 6,017 | |
1.1% | 1.7% | |
9.9 | 8.2 | |
2 days ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
hypothesis
- Hypothesis
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Hypothesis for Property-Based Testing: Hypothesis is a Python library facilitating property-based testing. It offers a distinct advantage by generating a wide array of input data based on specified properties or invariants within the code. The perks of Hypothesis include:
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Pix2tex: Using a ViT to convert images of equations into LaTeX code
But then add tests! Tests for LaTeX equations that had never been executable as code.
https://github.com/HypothesisWorks/hypothesis :
> Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.
> This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.
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pgregory.net/rapid v1.0.0, modern Go property-based testing library
pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.
- Was muss man als nicht-technischer Quereinsteiger in Data Science *wirklich* können?
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Python toolkits
Hypothesis to generate dummy data for test.
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Best way to test GraphQL API using Python?
To create your own test cases, I recommend you use hypothesis-graphql in combination with hypothesis. hypothesis is a property-based testing library. Property-based testing is an approach to testing in which you make assertions about the result of a test given certain conditions and parameters. For example, if you have a mutation that requires a boolean parameter, you can assert that the client will receive an error if it sends a different type. hypothesis-graphql is a GraphQL testing library that knows how to use hypothesis strategies to generate query documents.
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Fuzzcheck (a structure-aware Rust fuzzer)
The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.
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Running C unit tests with pytest
We've had a lot of success combining that approach with property-based testing (https://github.com/HypothesisWorks/hypothesis) for the query engine at backtrace: https://engineering.backtrace.io/2020-03-11-how-hard-is-it-t... .
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Machine Readable Specifications at Scale
Systems I've used for this include https://agda.readthedocs.io/en/v2.6.0.1/getting-started/what... https://coq.inria.fr https://www.idris-lang.org and https://isabelle.in.tum.de
An easier alternative is to try disproving the statement, by executing it on thousands of examples and seeing if any fail. That gives us less confidence than a full proof, but can still be better than traditional "there exists" tests. This is called property checking or property-based testing. Systems I've used for this include https://hypothesis.works https://hackage.haskell.org/package/QuickCheck https://scalacheck.org and https://jsverify.github.io
bandit
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Enhance Your Project Quality with These Top Python Libraries
Bandit is a tool designed to find common security issues in Python code. It was developed by the OpenStack Security Project and is a great addition to any serious Python project.
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Creating a DevSecOps pipeline with Jenkins — Part 1
For the SAST stage, I used SonarQube tool. SonarQube is an open-source platform developed by SonarSource for continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs and code smells on more than 30 programming languages. I preferred SonarQube instead of other SAST tools because it has a detailed documentation and plugins about integration with Jenkins and SonarQube works with Java projects pretty well. Of course you can similar multi-language-supported tools such as Semgrep or language-specific tools such as Bandit.
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Enhance your python code security using bandit
repos: - repo: https://github.com/PyCQA/bandit rev: 1.7.7 hooks: - id: bandit args: ["-c", "pyproject.toml", "-r", "."] additional_dependencies: ["bandit[toml]"]
- Show HN: Codemodder – A new codemod library for Java and Python
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
On the other hand, Bandit is a dedicated security scanner designed to target critical security concerns such as SQL injection and cross-site scripting exploits. It meticulously scrutinizes the codebase to identify and alert developers about possible security breaches or vulnerabilities, thus fortifying the code against potential exploitation.
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The Uncreative Software Engineer's Compendium to Testing
Bandit: is a tool designed for Python applications to analyse your code for potential security issues like insecure use of functions, hardcoded password and much more.
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The 36 tools that SaaS can use to keep their product and data safe from criminal hackers (manual research)
Bandit (for Python, open-source and free)
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Which CI/CD learn first?
Add security checks (Bandit) and dependency checks (safety)
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Why are python coding standards such a mess, what is everything and where do I start?
bandit
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Python toolkits
flake8-bandit which uses bandit for security linting.
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Flake8 - flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
Robot Framework - Generic automation framework for acceptance testing and RPA
pre-commit-hooks - Some out-of-the-box hooks for pre-commit
Behave - BDD, Python style.
safety - Safety checks Python dependencies for known security vulnerabilities and suggests the proper remediations for vulnerabilities detected.
nose2 - The successor to nose, based on unittest2
flake8-bandit - Automated security testing using bandit and flake8.
nose - nose is nicer testing for python
black - The uncompromising Python code formatter
Schemathesis - Supercharge your API testing, catch bugs, and ensure compliance
mypy - Optional static typing for Python